INFORMATICS PRACTICES - Inventory Management System L ATHUL TOM MATHEW L Roll No. 6 Class : XII Submitted to:CERTIFICATE It is hereby certified that Mast./Miss Athul Tom Mathew of class XH has carried out the necessary practical work for the subject Informatics Practices as per the syllabus prescribed by the Central Board of Senior Education, New Dethi, for the year 2021-22. Prbbasbsioa Y LAgipe Internal Invigilator irnal Invigilator Date: 10/01/2022 GRE SENTATS ches School SealFirst and foremost, | would like to extend my sincere thanks and gratitude to my Informatics teacher Mrs.Naga Subhashini.B and our principal Mr.V.S. Suresh who gave me the golden opportunity to do this wonderful project which has provided valuable information about ‘USED CARS- INVENTORY SYSTEM’ which has also helped me in doing a lot of Research and learn about many new things. I am really thankful to them for giving valuable time and moral support to develop this software. Also, I would like to take the opportunity to thank my family and friends for their sincere support, who helped in gathering different information, guiding me from time to time and providing all the essential requirements in making this project a successful one and without whom I couldn’t have succeeded in completing this task. Last but not the least, I would like to thank my school and everyone who helped and motivated me in completing this project with utmost perfection, within the limited time frame.Cera) ey Page no. a sce alec a a rome a] nsThe Used Cars- Inventory Management GUI models an interface that is designed to let the user gain insights and summary statistics on listed used cars. The user can get reports on car brands, available colors, average price for each brand, which area/state lists the most used cars,..cte,. Various data analysis techniques and visualizations were implemented to facilitate the process for the user to get the information they want visualized and clear to understand. ‘The dataset used in this project is limited to cars in USA, however it can be extended to work in any area where data of used cars are available. In this project two datasets were used (collected from Kaggle). The two data sets were joined using pandas functions and analytics were done on the full merged data. This interface was built on Jupyter notebook using modules like pandas, matplotlib and numpy, however it can work on any python supporting environment.BOURCE CODE |Importing Libraries import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import time File paths csv_file_1 ='used_cars_l.csv' # File Path esv_file_2 ='nsed_cars_2.csv' # Second file path Application def preprocessing(dfl, df2): "This function is to preprocess data before analysis """ # Droping duplicate columns df2,drop(columns = ['price’, ‘brand’), inplace = True) #Joining both tables df = pd.concat([dfl, df2], axis=1, join="inner") # Dropping unuseful column df-drop(columns = ['Unnamed: 0'], inplace = True) a# Dropping rows with invalid values for price df.drop(df[df'price] — 0].index, inplace = True) return df def introduction(): msg="" This project is to help people get summary statistics and get insights from data visulaizations for used cars before buying. ‘The dataset contains the list of used cars listed in USA. There are two files containing the data, this project imports both files and merge them to deal with them as whole data In this project I'm going to analyse the same dataset using Python Pandas on windows machine but the project can be run on any machine support Python and Pandas. Besides pandas we also used matplotlib and seaborn modules for visualization of this dataset. The whole project is divided into four major parts ie reading, analysis, visualization and export. all these part are further divided into menus for easy navigation\n\n\nin" for x in msg: print(x,end =") time.sleep(0.000001) wait = input(Press any key to continue. def made_by() msg="" UsedCars Project made by : ATHUL TOM MATHEW Roll No : 6/ GR No: 1521 School Name : Indian School Ibri session 2021-22 Thanks for evaluating my Project. \n\n\n for x in msg: print(x, end=")time.sleep(0,000001) wait = input('Press any key to continue.. defread_csv file(csv_file); df=pd.read_csv(csv_file) print(df) def data_analysis_menu(): dfl = pd.read_esv(esv_file_1) df2 = pd.read_esv(csv_file_2) df= preprocessing(dfl, df2) while True: print('\n\nData Analysis MENU’) print(’_*100) Print('l. Show Whole DataFrame\n') print('2. Show Columns\n') Print('3. Show Top Rows\n'y print('4, Show Bottom Rows\n') print('S. Show Specific Column\n')print('6. Add a New Record\n’) print(7. Add a New Column\n’) print('8. Delete a Column\n’) print('9, Delete a Record\n’) print('10. Update a Record\n') print('I1. Car Brands Report \n') print('12. Available Colors Report \n') print('13. Data Summary\n’) print('14. Exit (Move to main menu)\n') ch = int(input('Enter your choice:')) ifch= print(df) wait = input() iftch print(df.columns) wait = input() if ch ==3: n= int(input(Enter Total rows you want to show :')) print(df.head(n)) wait = input()if ch == 4: n = int(input('Enter Total rows you want to show :')) print(df.tail(n)) wait — input() ifch == 5: print(df.columns) col_name = input(‘Enter Column Name that You want to print :') print(df[col_name]) wait = input() if ch==6: a = int(input('Enter price :')) b = input(‘Enter brand name :’) c= input(' Enter model name :’) d= int(input('Enter year of model :')) e = input(Enter the car status :') f= int(input('Enter mileage of the car :')) g~ input('Enter vin: ") h = int(input(‘Enter lot :')) i =input('Enter the state where the car is listed :') j= input(Enter country where the car is listed : k = input(Enter the car condtion * We ir‘brand':b,'model':,'year'sd,title_status':e,'mileage'f'vin':g + 'lot'sh,'state'si, country, ‘condition'sk} df= df.append(data,ignore_index=True) print(df) wait=input() if ch: col_name = input('Enter new column name :') col_value = input('Enter default column value :) dffeol_name]=col_value print(df) print(‘\n\n\n Press any key to continue... col_name =input('Enter column Name to delete :’) del dflcol_name] print(df) print('\n\n\n Press any key to continue. wait=input() aeif ch==9: index_no =int(input(‘Enter the Index Number that You want to delete :')) df= df.drop(df.index[index_no]) print(df) print('\n\n\n Press any key to continue...) wait = input() if ch—=10: index_no =int(input('Enter the Index Number that You want to update :')) col_name = input('Enter the column name that You want to update :') new_val = input('Enter the new value you want to update :) df.at[index_no, col_name]= new_val print(df) print('\n\n\n Press any key to continue...) wait = input()print(‘Available brands :',df1) print(‘\n\n') brandName =input('Enter the brand you want a) dfl=df[df.brand—=brandName] print(dfl) print(‘\n\n\n Press any key to continue....') wait = input() dfl=df.color.unique() print('Available colors :',df1) print('\n\n') color_=input('Enter the color you want :) dfl=df[df.color==color_] print(dfl) print(\n\n\n Press any key to continue. wait = input() if ch=13: print(df.describe()) print("\n\n\nPress any key to continuwait=input() ifch = 14: break def graph(): afl = pd.read_esv(csv_file_1) df2 = pd.read_csv(csy_file 2) df= preprocessing(dfl, df2) while True: print(\nData visualization menu(Graphs) ') print(_'*100) print('l. Line Graph For Average Price For Fach State\n’) print('2. Bar Chart Showing Top 10 States Selling Cars\n’) print('3. Scatter Plot Between Mileage and Price\n') print('4. Pie Chart for Brands\n') print('5. Bar Graph for Average Price Per Brand\n') print('6, Bar Graph for top 10 selling brands\n') print(’7. Exit (Move to main menu)\n') ch = int(input(‘Enter ae alif ch= 2 = df.groupby(state’) x= dff'state'].unique() y = g['price’].mean() plt-xticks(rotation='vertical') plt.xlabel('State') plt.ylabel('Average Price’) plt.title(‘Average price for each state’) plt.grid(True) plt.plot(x, y) plt.show(, if ch==2: fig,ax= plt.subplots() fig.set_size_inches(25,10) states_n pd.DataFrame(df|"state"].value_counts()).reset_index() plt.xticks(rotation=45) sns.barplot(data=states_n.head(10),x="index",y="state" ax=ax) ax.set(xlabel'state’, ylabel='Count’ title="Top 10 selling _ a aeplt.show() wait = input(Press Any Key To Proceed’) if ch==3: fig,ax= plt.subplots() fig.set_size_inches(15,5) sns.scatterplot(data=df, x="mileage", y="price", hue="title_status") plt.show() wait = input(Press Any Key To Proceed’) if ch—=4: brand_name = input(‘Please Enter the brand you want to visualize: ' dfl = di[df.brand = brand_name] df2 = dfl['model'].value_counts() fig = df2.plot pie() pit.show() wait = input(Press Any Key To Proceed’)plt.figure(figsize=(10,6)) ax = sns.barplot(x="brand', y='price’, data=df) ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha="right",fontsize=8) plt.title("Car manufacturer vs average price") plt.xticks(rotation="'vertical’) plt.show() wait= input(‘Press Any Key To Proceed’) fig,ax= plt.subplots() fig.set_size_inches(15,5) brand_of_car- df. groupby(‘brand’)['mode!'].count().reset_index().sort_values('model',as cending = False).head(10) brand_of_car ~ brand_of_car.rename(columns = {'model':‘count’}) ax = sns.barplot(x="brand", y="count", data=brand_of car) plt.show()def export_menu(): dfl =pd.read_csv(csv_file_1) df2 = pd.read_csv(csv_file 2) df = preprocessing(dfl, df2) while True: print(\n\nEXPORT MENU ') print('_'*100) print() print('l. CSV File\n') print('2, Excel File\n') print('3. Exit (Move to main menu)’) ch = int(input(‘Enter your Choice :')) if ch= df-to_csv(‘project.csv’) print(‘\n\nCheck your new file "project.csv" on the same path where you run this application’) wait = input(Press Any Key —ifch == 2: df-to_excel('project.xlsx") print("'n\nCheck your new file "project.csv" on the same path where you run this application’) wait = input(‘Press Any Key To Proceed’) ifch == def main_menu() introduction() while True: print(MAIN MENU ') print(_*100) print) print('l. Read CSV File\n') print('2. Data Analysis Menu\n’) print'3. Graph Menu\n'yprint('4. Export Data\n') print('S. Exit\n') choice = int(input(Enter your choice :)) if choice==1: print('Please choose which data do you want to diplay?\n') print('l. dataset no.1 \n') print('2. dataset no. 2\n') print('3. Merged data’) user_choice = int(input()) afl = pd.tead_esv(csv_file_1) df2 = pd.tead_csv(csy_file 2) df= preprocessing(dfl, df2) if user_choice == 1: print(dfl) 1 aelif user_choice print(df2) f 2: 1 elif user_choice == 3: print(df) else: print(‘Invalid Choice, please choose a valid option ') wait=input(Press Any Key To Proceed’) if choice==2: print('Data Analysis Menu’) data_analysis_menu() wait=input('Press any key to proceed’) if choics : graph() wait=input('Press Any Key To Proceed’) y- weif choice: export_menu() wait=input('Press Any Key To Proceed’) made_by() main_menu()ss © pes WZ 2 ee) ee) ed ) oyA. dataset ro lack rea silver cage Fore jtecuziteskeo7res Bnd gesbeb02227 Sededeggsjeseecis af tfutetdete23745 Sacpcrec2g473592 anten7assiageesis snten7ap5j1e84e88 ‘snuen7aps3isesie 3nen79p331885263 3nien7apajleaast1 3 column 3500 zen pois zee aes aeis 2e18 ciean a mileageRead SV Falk bata visualization menu(Grapne) Exzort De exit ter your choice :2 ease choose nich gata do you want to diplay? aatazet no jerged date price brand model year title_statvs nileage color 6302 toyota cruiser 2006 clean vehicle 274117 black Fors se 2811 clean venice 190552. silver dodge mpv 2018 clear vehicle 39550. silver ford door 2614 clean vanicle aide blue chevrolet 1500 2618 clean vehicle 5654 red nissan versa 2819 clean vehicle red nissan verea 2018 clean vehicle siver nissen versa 2018 clean vehicle 31584 silver nissan versa 2018 clean vensel black nissan vera 2818 clear vehicle silver eeru2ifeskeo7763 15934787 new jersey usa 28 days Deraksgeatbvezz17 iss9s1z62 tennessee Usa aye Bespdeggsjts4eai3 167655725 gecrgia uta 2 days 1 Lcfunetsefe2s745 167755855 virginia usa 22 hours J Bacpcrecdjgi73901 167768268 florida. usa 32 hours snien7apskiesesi9 167722715 caliternia Use days Snlcn7ap5j1884088 167762225 florida usa 21. hours, Snlen7apsjleadis1 167762226 © flerida usa 21. hours, Sraen?epajieaz63 167762227 use 2 days Bnlen7apaji684311 167762221 usa 22 hours rows x 42 columns]Read CSV File Data analysis Menu Data visualization menu(Gra Export Data Enter your choice Data analysis Menu Analysis MENU iWiole DataFrane columns Botton Rows Show Specific Colunn Add a New Record Add a New Colum elete a Colum belete « Recore Update a Record ar Brands Report Available Colors Report Data Sumary Exit (ove te main menu)Data Anelysis wENU i.” $ROW Whole DataFrane 2. show columns Show Top Rows 4. 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Car Brands Report 4. Exit (Nove te main nenv) your choice:12 ['black* "silver’ “biue’ ‘rea ‘toreador red" 'o gray’ ‘orange’ ‘arena’ ‘no_coler blue pearicos iple yellow tri “shadow black y red metallic tinted "competition ors oft metallic! 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Bar Chart Showing Top 10 States Selling cars 3. Scatter Plot Between fileage and Price 4. Pie chart for Brands Ber Graph for Average Price Per Brand Bar Graph for too 16 selling brands Frit (Move to main menu) ter your choice:a Please Enter the brand you want to visualize bor Press Any Key To Praceed[| J MeData visualization menu(Graphs) 1. Ulne Graph For Average Price For Each State” 2. Gar Chart Showing Top 16 States Selling Cars Scatter Plot Between Mileage and Price Pie Chart for Brands Bar Graph for Average Price Per Brand Bar Graph for top 10 selling brands Exit (Hove to main menu) Enter your cheice:s ht | i. | J) il Press Any Key To Proceedeas csv File Date analysis Menu Data visualization menu(Graph Export pata Bait Entes your choice ExpoRT menu (SV File 2. fxcel File Bxit (Nove to main menu) nter your Choice 1 Check your neu file “project.csv" on the sane path where you run this application Press Any Key To Proceed|]_ = exPORT MENU a. csv File fxcel File 3. Exit (Hove to main menu) Enter your chotce 2 Grech Your new file “project.x1sx" on the sane path where you run this application Press any Key To ProceedMAIN MENU Read CSV Fite Data Analysis Menu Data visualization nenu(craphs) Export bata eat Enter your choice :5 UsedCars Project made by + ATHUL Tow MATHEW Roll No 6/ GR No + 1szt School Name ndian School. Tori session 2021-22 Thanks for evaluating my Project. Press any Key to continue, ....[[——————_—_—___ aE+ Class 12 Information Practices textbook (by: Sumita Arora) > Class 11 Information Practices textbook (by: Sumita arora) *& Kaggle.com (For data collection) > hups:/towardsdatascience.com/a-beginners-guide-to-data- isualization-with-python-49f1d257¢781 (Visualization Article) 4 Python for Data Analysis, by Wes McKinney (Book) Other than the above-mentioned books and websites, the suggestions and supervision of my teacher and my class experience also helped me to develop this software project.